Importing the data

  • R is efficient at handling data in “long” format: each column is a variable and repeated measures are in separate rows (Excel tables are often use “wide” format)
  • The prep_data() function imports and combines the data contained in the vd, vds, and vde file. This generates a single long R dataframe (tibble). All strings are in lower case, missing data is replaced with NA (or “annual” for timeslice).
  • Here, we import all the data from demos_001 to create a dataframe object demos_001, and display the data in an interactive table demos_001

Comparing the data to veda tables

  • We can compare results produced by VedaR to those produced from tables generated by the Veda graphical-user interface.

  • Here, we compare the output of the “All costs” Veda table to comparable output from VedaR.

Veda csv output
Attribute Commodity Process Period Region Pv
Cost_Flo [Annual flow costs (including import/export prices)] COA EXPCOA1 2005 REG1 -3154.440
Cost_Flo [Annual flow costs (including import/export prices)] COA EXPCOA1 2006 REG1 -3154.440
Cost_Flo [Annual flow costs (including import/export prices)] COA IMPCOA1 2005 REG1 17772.345
Cost_Flo [Annual flow costs (including import/export prices)] COA IMPCOA1 2006 REG1 17772.345
Cost_Flo [Annual flow costs (including import/export prices)] COA MINCOA1 2005 REG1 12147.537
Cost_Flo [Annual flow costs (including import/export prices)] COA MINCOA1 2006 REG1 12147.537
Cost_Flo [Annual flow costs (including import/export prices)] COA MINCOA2 2005 REG1 5061.474
Cost_Flo [Annual flow costs (including import/export prices)] COA MINCOA2 2006 REG1 5061.474
Cost_Fom [Annual fixed operating and maintenance costs]
DTPSCOA 2005 REG1 2823.992
Cost_Fom [Annual fixed operating and maintenance costs]
DTPSCOA 2006 REG1 2823.992
Cost_Inv [Annual investment costs]
DTPSCOA 2005 REG1 11057.162
Cost_Inv [Annual investment costs]
DTPSCOA 2006 REG1 11057.162
Cost_Salv [Salvage values of capacities at EOH+1]
DTPSCOA
REG1 113069.549
VedaR data
attribute commodity process period region pv
cost_flo coa expcoa1 2005 reg1 -3154.440
cost_flo coa expcoa1 2006 reg1 -3154.440
cost_flo coa impcoa1 2005 reg1 17772.345
cost_flo coa impcoa1 2006 reg1 17772.345
cost_flo coa mincoa1 2005 reg1 12147.537
cost_flo coa mincoa1 2006 reg1 12147.537
cost_flo coa mincoa2 2005 reg1 5061.474
cost_flo coa mincoa2 2006 reg1 5061.474
cost_fom NA dtpscoa 2005 reg1 2823.992
cost_fom NA dtpscoa 2006 reg1 2823.992
cost_inv NA dtpscoa 2005 reg1 11057.162
cost_inv NA dtpscoa 2006 reg1 11057.162
cost_salv NA dtpscoa NA reg1 113069.549

Data Visualisation with R

  • A powerful and widely-used data visualisation package in R is ggplot
  • Here, we create a plot to display compare costs over years broken down by process, from the demos_001 data

  • We can create a plot for a different dataset by simply changing the data that is passed to the plot.
  • Here, we plot the demos_007 data by changing the object data_to_plot. All other lines remaing the same

  • The ggplotly can create interactive plots
  • Here, we create an point plot of the demos_007, var_act variable for “elc” processes

Visualising the energy system

  • Sankey diagrams can be used to visualise flows between elements in a connected system.
  • The VedaR function make_res() visualises the system of the TIMES solution as a Sankey diagram.
  • In the current version, the magnitude of flows is not represented, and all the widths are set to a constant value.
res_all <- make_res(dat = demos_007,
                    region_select = "reg1",
                    period_select = 2020,                 
                    node_labels = process_description, 
                    edge_labels = commodity_description, 
                    sankey_width = 1000,
                    sankey_height = 1000,
                    font_size = 10)
## <quosure>
## expr: ^process_description
## env:  global
res_all

Representing the energy system as a network

  • “Graphs” are formal representations of connected networks. Graphs are comprised of nodes and edges. Nodes and edges can be described by sets of attributes, e.g. Edges can have weights.
  • R has tools for efficient analysis of graph structures. The igraph package is one of these https://igraph.org/r/html/latest/.
  • The VedaR function make_graph_from_veda_df() transforms the veda data tibble that was produced by prep_data() into an igraph object. If the data contains only a single year, edge weights are assigned based on var_fin and var_fout attributes.
  • Here, we convert the demos_007 data for 2020 and reg1 to an igraph object.
g <- demos_007 %>%
      filter(period == 2020, 
             region == "reg1") %>%
      make_graph_from_veda_df(node_labels = process,
                              edge_labels = commodity
                              )

# print edge weights
print("Edge weights")
## [1] "Edge weights"
E(g)$weight
##  [1]   2624.76319   3091.68686     47.10103    458.01812   1255.69200
##  [6]   1842.83904   2669.57081    954.51146 478946.18800 634340.87421
## [11]   1255.69200   4799.06000   6356.12098    985.97983     15.02114
## [16]    146.06803    900.38520    743.56800    177.23310    116.70990
## [21]    240.50400    989.09130    271.82160   9588.59790    146.07973
## [26]   1420.50335   1255.69200   2671.26734   2450.22180   4963.26911
## [31]   5121.48914   3596.80600   6050.35198 378990.19673

What are the paths from, or between processes?

  • We can compute all simple paths from a given node using the igraph function all_simple_paths() (or between two nodes if the to = argument is specified)

  • Here, we create an object all_mincoa1_paths that is the list of all paths starting from the mincoa1 process.

all_mincoa1_paths <- all_simple_paths(g, from = "mincoa1")
all_mincoa1_paths
## [[1]]
## + 2/30 vertices, named, from ca65929:
## [1] mincoa1    fte-elccoa
## 
## [[2]]
## + 3/30 vertices, named, from ca65929:
## [1] mincoa1    fte-elccoa elctecoa00
## 
## [[3]]
## + 4/30 vertices, named, from ca65929:
## [1] mincoa1    fte-elccoa elctecoa00 dtpselc   
## 
## [[4]]
## + 5/30 vertices, named, from ca65929:
## [1] mincoa1            fte-elccoa         elctecoa00         dtpselc           
## [5] tpselc_end_process
## 
## [[5]]
## + 4/30 vertices, named, from ca65929:
## [1] mincoa1            fte-elccoa         elctecoa00         elcco2_end_process
## 
## [[6]]
## + 3/30 vertices, named, from ca65929:
## [1] mincoa1    fte-elccoa elctncoa00
## 
## [[7]]
## + 4/30 vertices, named, from ca65929:
## [1] mincoa1    fte-elccoa elctncoa00 dtpselc   
## 
## [[8]]
## + 5/30 vertices, named, from ca65929:
## [1] mincoa1            fte-elccoa         elctncoa00         dtpselc           
## [5] tpselc_end_process
## 
## [[9]]
## + 4/30 vertices, named, from ca65929:
## [1] mincoa1            fte-elccoa         elctncoa00         elcco2_end_process
## 
## [[10]]
## + 2/30 vertices, named, from ca65929:
## [1] mincoa1 dtpscoa
## 
## [[11]]
## + 3/30 vertices, named, from ca65929:
## [1] mincoa1            dtpscoa            tpscoa_end_process
## 
## [[12]]
## + 2/30 vertices, named, from ca65929:
## [1] mincoa1 expcoa1

Does a specified process appear in a set of paths?

  • The vedar function check_in_paths() provides a means of checking whether a string expression is included in the set of paths. The string needs to be passed as a (regular expression)[https://www.petefreitag.com/cheatsheets/regex/]

  • If all export processes include the string “exp”, you can check if the string “exp” appears in the set of paths “all_mincoa1_paths” using the following command.

check_in_path("(exp)", all_mincoa1_paths)
## [[1]]
## [1] FALSE FALSE
## 
## [[2]]
## [1] FALSE FALSE FALSE
## 
## [[3]]
## [1] FALSE FALSE FALSE FALSE
## 
## [[4]]
## [1] FALSE FALSE FALSE FALSE FALSE
## 
## [[5]]
## [1] FALSE FALSE FALSE FALSE
## 
## [[6]]
## [1] FALSE FALSE FALSE
## 
## [[7]]
## [1] FALSE FALSE FALSE FALSE
## 
## [[8]]
## [1] FALSE FALSE FALSE FALSE FALSE
## 
## [[9]]
## [1] FALSE FALSE FALSE FALSE
## 
## [[10]]
## [1] FALSE FALSE
## 
## [[11]]
## [1] FALSE FALSE FALSE
## 
## [[12]]
## [1] FALSE  TRUE
  • More complex queries can be created using igraph functions. Here, we check whether an “exp” process is included in the paths that are linked to the “coa” commodity as follows
#find all coa edges
coa_edges <- which(E(g)$commodity == "coa")

#find the start vertices of the coa commodity edges
coa_start_vertices <- ends(g, coa_edges)[,1]

#find all the paths that are linked to the coa_start_vertices

all_coa_paths <- all_simple_paths(g, from = unique(coa_start_vertices))

#check if the string "exp" appears in the set of paths that project from coa_start_vertices
print("Check if 'exp' is in all_coa_paths")
## [1] "Check if 'exp' is in all_coa_paths"
check_in_path("(exp)", all_coa_paths)
## [[1]]
## [1] FALSE FALSE
## 
## [[2]]
## [1] FALSE FALSE FALSE
## 
## [[3]]
## [1] FALSE FALSE FALSE FALSE
## 
## [[4]]
## [1] FALSE FALSE FALSE FALSE FALSE
## 
## [[5]]
## [1] FALSE FALSE FALSE FALSE
## 
## [[6]]
## [1] FALSE FALSE FALSE
## 
## [[7]]
## [1] FALSE FALSE FALSE FALSE
## 
## [[8]]
## [1] FALSE FALSE FALSE FALSE FALSE
## 
## [[9]]
## [1] FALSE FALSE FALSE FALSE
## 
## [[10]]
## [1] FALSE FALSE
## 
## [[11]]
## [1] FALSE FALSE FALSE
## 
## [[12]]
## [1] FALSE  TRUE
#check if the string "dist" appears in the set of paths that project from coa_start_vertices
print("Check if 'dist' is in all_coa_paths")
## [1] "Check if 'dist' is in all_coa_paths"
check_in_path("(dist)", all_coa_paths)
## [[1]]
## [1] FALSE FALSE
## 
## [[2]]
## [1] FALSE FALSE FALSE
## 
## [[3]]
## [1] FALSE FALSE FALSE FALSE
## 
## [[4]]
## [1] FALSE FALSE FALSE FALSE FALSE
## 
## [[5]]
## [1] FALSE FALSE FALSE FALSE
## 
## [[6]]
## [1] FALSE FALSE FALSE
## 
## [[7]]
## [1] FALSE FALSE FALSE FALSE
## 
## [[8]]
## [1] FALSE FALSE FALSE FALSE FALSE
## 
## [[9]]
## [1] FALSE FALSE FALSE FALSE
## 
## [[10]]
## [1] FALSE FALSE
## 
## [[11]]
## [1] FALSE FALSE FALSE
## 
## [[12]]
## [1] FALSE FALSE

Other queries

  • igraph allows you to extract a range of graph theory metrics from your TIMES graph.
  • See the the package documentation for details.